1. PCM & DPCM & DM

2. Pulse-Code Modulation (PCM) :
In PCM each sample of the signal is quantized to one of the amplitude levels, where B is the number of bits used to represent each sample.
The rate from the source is bps.
The quantized waveform is modeled as :
q(n) represent the quantization error, Which we treat as an additive noise.

3. Pulse-Code Modulation (PCM) :
The quantization noise is characterized as a realization of a stationary random process q in which each of the random variables q(n) has uniform pdf.
Where the step size of the quantizer is

4. Pulse-Code Modulation (PCM) :
If :maximum amplitude of signal,
The mean square value of the quantization error is :
Measure in dB, The mean square value of the noise is :

5. Pulse-Code Modulation (PCM) :
The quantization noise decreases by 6 dB/bit.
If the headroom factor is h, then
The signal to noise (S/N) ratio is given by
In dB, this is

6. Pulse-Code Modulation (PCM) :
Example :
We require an S/N ratio of 60 dB and that a headroom factor of 4 is acceptable. Then the required word length is :
60= B – 20
If we sample at 8 kHz, then PCM require

7. Pulse-Code Modulation (PCM) :
A nonuniform quantizer characteristic is usually obtained by passing the signal through a nonlinear device that compress the signal amplitude, follow by a uniform quantizer.
Compressor
A/D
D/A
Expander
Compander
(Compressor-Expander)

8. Companding: Compression and Expanding
Original Signal
After Compressing, Before Expanding

9. Companding
A logarithmic compressor employed in North American telecommunications systems has input-output magnitude characteristic of the form
is a parameter that is selected to give the desired compression characteristic.

10. Companding

11. Companding
The logarithmic compressor used in European telecommunications system is called A-law and is defined as

12. Companding

13. DPCM : A Sampled sequence u(m), m=0 to m=n-1.
Let be the value of the reproduced (decoded) sequence.

14. DPCM:
At m=n, when u(n) arrives, a quantify , an estimate of u(n), is predicted from the previously decoded samples i.e.,
”prediction rule”
Prediction error:

15. DPCM :
If is the quantized value of e(n), then the reproduced value of u(n) is:
Note:

16. DPCM CODEC: Σ
Quantizer
Communication
Channel
Predictor
Coder
Decoder

17. DPCM:
Remarks:
The pointwise coding error in the input sequence is exactly equal to q(n), the quantization error in e(n).
With a reasonable predictor the mean sequare value of the differential signal e(n) is much smaller than that of u(n).

18. DPCM:
Conclusion:
For the same mean square quantization error, e(n) requires fewer quantization bits than u(n).
The number of bits required for transmission has been reduced while the quantization error is kept the same.

19. DPCM modified by the addition of linearly filtered error sequenceΣ
Quantizer
Communication
Channel
Linear filter
Coder
Decoder
Linear
filter

20. Adaptive PCM and Adaptive DPCM
Speech signals are quasi-stationary in nature
The variance and the autocorrelation function of the source output vary slowly with time.
PCM and DPCM assume that the source output is stationary.
The efficiency and performance of these encoders can be improved by adaptation to the slowly time-variant statistics of the speech signal.
Adaptive quantizer
feedforward
feedbackward

21. Example of quantizer with an adaptive step size∆ 2∆ 3∆ -∆
-2∆
-3∆
∆/2
3∆/2
5∆/2
7∆/2
-∆/2
-3∆/2
-5∆/2
-7∆/2
M (1)
M (2)
M (3)
M (4)
000
001
010
011
100
101
110
111
Previous Output
Multiplier

22. ADPCM with adaptation of the predictor
Step-size
adaptation
Communication
Channel Σ
Quantizer
Encoder
Decoder Σ Σ
Predictor
Predictor
Predictor
adaptation
Coder
Decoder

23. Delta Modulation : (DM)
Predictor : one-step delay function
Quantizer : 1-bit quantizer

24. Delta Modulation : (DM)
Primary Limitation of DM
Slope overload : large jump region
Max. slope = (step size)X(sampling freq.)
Granularity Noise : almost constant region
Instability to channel noise

25. DM:
Unit Delay
Integrator
Coder
Unit Delay
Decoder

26. DM: Step size effect : Step Size (i) slope overload
(sampling frequency ) (ii) granular Noise

27. Adaptive DM:
Adaptive
Function
Unit Delay
This adaptive approach simultaneously minimizes the effects of both slope overload and granular noise

28. Vector Quantization (VQ)

29. Vector Quantization :
Quantization is the process of approximating continuous amplitude signals by discrete symbols.
Partitioning of
two-dimensional
Space into 16 cells.

30. Vector Quantization :
The LBG algorithm first computes a 1-vector codebook, then uses a splitting algorithm on the codeword to obtain the initial 2-vector codebook, and continue the splitting process until the desired M-vector codebook is obtained.
This algorithm is known as the LBG algorithm proposed by Linde, Buzo and Gray.

31. Vector Quantization : The LBG Algorithm :
Step 1: Set M (number of partitions or cells)=1.Find the centroid of all the training data.
Step 2: Split M into 2M partitions by splitting each current codeword by finding two points that are far apart in each partition using a heuristic method, and use these two points as the new centroids for the new 2M codebook. Now set M=2M.
Step 3: Now use a iterative algorithm to reach the best set of centroids for the new codebook.
Step 4: if M equals the VQ codebook size require, STOP; otherwise go to Step 2.